Citation:

Srikanth Reddy Katta, 2024. "AI-Powered Optimization of Multi-Site Pharmaceutical Manufacturing Calibration Intervals", ESP Journal of Engineering & Technology Advancements  4(4): 172-180.

Abstract:

The pharmaceutical manufacturing industry is characterized by strict quality control requirements and a requirement for close equipment calibration for achieving the product integrity and successful regulatory compliance. In multiple manufacturing site manufacturing, managing calibration intervals is a huge challenge leading to downtime and inconsistency. In this study we investigate the use of Artificial Intelligence (AI) for optimizing calibration schedules in multi-site operations. The proposed AI framework uses machine learning algorithms to process historical calibration data and real time equipment performance metrics to forecast optimal interval between calibrations. This predictive approach moves the industry from conventional, time based calibration methods to data based, condition based ones. Satisfactory results have been achieved in terms of operational efficiency, reduced disruptions and increased conformance to quality control standards. Furthermore, the framework supports robust traceability and documentation, so important to regulatory audits. These improvements in calibration precision, regulatory compliance, and cost savings are illustrated with case studies in the paper. The findings highlight how AI has the transformative potential to not only meet future industry demand, but to be a scalable solution to deliver operational excellence.

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Keywords:

AI, Pharmaceutical Manufacturing, Machine Learning, Operational Efficiency, Quality Assurance, Regulatory Compliance.